To support the assessments of the Intergovernmental Science-Policy Platform
on Biodiversity and Ecosystem Services (IPBES), the IPBES Expert Group on
Scenarios and Models is carrying out an intercomparison of biodiversity and
ecosystem services models using harmonized scenarios (BES-SIM). The goals of
BES-SIM are (1) to project the global impacts of land-use and climate change
on biodiversity and ecosystem services (i.e., nature's contributions to
people) over the coming decades, compared to the 20th century, using a set of
common metrics at multiple scales, and (2) to identify model uncertainties
and research gaps through the comparisons of projected biodiversity and
ecosystem services across models. BES-SIM uses three scenarios combining
specific Shared Socio-economic Pathways (SSPs) and Representative
Concentration Pathways (RCPs) – SSP1xRCP2.6, SSP3xRCP6.0, SSP5xRCP8.6 – to
explore a wide range of land-use change and climate change futures. This
paper describes the rationale for scenario selection, the process of
harmonizing input data for land use, based on the second phase of the Land
Use Harmonization Project (LUH2), and climate, the biodiversity and ecosystem
services models used, the core simulations carried out, the harmonization of
the model output metrics, and the treatment of uncertainty. The results of
this collaborative modeling project will support the ongoing global
assessment of IPBES, strengthen ties between IPBES and the Intergovernmental
Panel on Climate Change (IPCC) scenarios and modeling processes, advise the
Convention on Biological Diversity (CBD) on its development of a post-2020
strategic plans and conservation goals, and inform the development of a new
generation of nature-centred scenarios.

Understanding how anthropogenic activities impact biodiversity and human
societies is essential for nature conservation and sustainable development.
Land-use and climate change are widely recognized as two of the main drivers
of future biodiversity change (Hirsch and CBD, 2010; Maxwell et al., 2016;
Sala, 2000; Secretariat of the CBD and UNEP, 2014), with potentially severe impacts on ecosystem services and ultimately
human well-being (Cardinale et al., 2012; MEA, 2005). Habitat and land-use
changes, resulting from past, present, and future human activities, as well
as climate change, have both immediate and long-term impacts on biodiversity
and ecosystem services (Graham et al., 2017; Lehsten et al., 2015; Welbergen
et al., 2008). Therefore, current and future land-use projections are
essential elements for assessing biodiversity and ecosystem change (Titeux et
al., 2016, 2017). Climate change has already been observed to have direct and
indirect impacts on biodiversity and ecosystems, which are projected to
intensify by the end of the century, with potentially severe consequences for
species and habitats, and, therefore, also for ecosystem functions and
services (Pecl et al., 2017; Settele et al., 2015).

Global environmental assessments, such as the Millennium Ecosystem Assessment
(MEA, 2005), the Global Biodiversity Outlooks (GBO), the multiple iterations
of the Global Environmental Outlook (GEO), the Intergovernmental Panel on
Climate Change (IPCC), and other studies have used scenarios to assess the
impact of socio-economic development pathways on land use and climate and
their consequences for biodiversity and ecosystem services (Jantz et al.,
2015; Pereira et al., 2010). Models are used to quantify the biodiversity and
ecosystem services impacts of different scenarios, based on climate and
land-use projections from general circulation models (GCMs) and integrated
assessment models (IAMs) (Pereira et al., 2010). These models include
empirical dose–response models, species–area relationship models, species
distribution models and more mechanistic models such as trophic ecosystem
models (Pereira et al., 2010; Akçakaya et al., 2015). So far, each of these scenario exercises has
been based on a single model or a small number of biodiversity and ecosystem
services models, and intermodel comparison and uncertainty analysis have been
limited (IPBES, 2016; Leadley et al., 2014). The Expert Group on Scenarios
and Models of the Intergovernmental Science-Policy Platform on Biodiversity
and Ecosystem Services (IPBES) is addressing this gap by carrying out a
biodiversity and ecosystem services model intercomparison with harmonized
scenarios, for which this paper lays out the protocol.

Over the past 2 decades, IPCC has fostered the development of global
scenarios to inform climate mitigation and adaptation policies. The
Representative Concentration Pathways (RCPs) describe different climate
futures based on greenhouse gas emissions throughout the 21st century (van
Vuuren et al., 2011). These emissions pathways have been converted into
climate projections in the most recent Climate Model Inter-comparison Project
(CMIP5). In parallel, the climate research community also developed the
Shared Socio-economic Pathways (SSPs), which consist of trajectories of
future human development with different socio-economic conditions and
associated land-use projections (Popp et al., 2017; Riahi et al., 2017). The
SSPs can be combined with RCP-based climate projections to explore a range of
futures for climate change and land-use change, and they are being used in a
wide range of impact modeling intercomparisons (Rosenzweig et al., 2017; van
Vuuren et al., 2014). Therefore, the use of the SSP-RCP framework for
modeling the impacts on biodiversity and ecosystem services provides an
outstanding opportunity to build bridges between the climate, biodiversity
and ecosystem services communities; it has been explicitly recommended as a
research priority in the IPBES assessment on scenarios and models (IPBES,
2016).

Model intercomparisons bring together different communities of practice for
comparable and complementary modeling, in order to improve the
comprehensiveness of the subject modeled, and to estimate uncertainties
associated with scenarios and models (Frieler et al., 2015). In the last
decades, various model intercomparison projects (MIPs) have been initiated to
assess the magnitude and uncertainty of climate change impacts. For instance,
the Inter-Sectoral Impact Model Intercomparison Project (ISI-MIP) was
initiated in 2012 to quantify and synthesize climate change impacts across
sectors and scales (Rosenzweig et al., 2017; Warszawski et al., 2014). The
ISI-MIP aims to bridge sectors such as agriculture, forestry, fisheries,
water, energy, and health with global circulation models, Earth system models
(ESMs), and integrated assessment models for more integrated and
impact-driven modeling and assessment (Frieler et al., 2017).

Here, we present the methodology used to carry out a BES-SIM in both
terrestrial and freshwater ecosystems. The BES-SIM project addresses the
following questions. (1) What are the projected magnitudes and spatial
distribution of biodiversity and ecosystem services under a range of land-use
and climate future scenarios? (2) What is the magnitude of the uncertainties
associated with the projections obtained from different scenarios and models?
Although independent of the ISI-MIP, the BES-SIM has been inspired by ISI-MIP
and other intercomparison projects and was initiated to address the needs of
the global assessment of IPBES. We brought together 10 biodiversity models
and six ecosystem functions and services models to assess impacts of land-use
and climate change scenarios in the coming decades (up to 2070) and to
hindcast changes to the last century (to 1900). The modeling approaches
differ in several respects concerning how they treat biodiversity and
ecosystem services responses to land-use and climate changes, including the
use of correlative, deductive, and process-based approaches, and in how they
treat spatial-scale and temporal dynamics. We assessed different classes of
essential biodiversity variables (EBVs), including species populations,
community composition, and ecosystem function, as well as a range of measures
on ecosystem services such as food production, pollination, water quantity
and quality, climate regulation, soil protection, and pest control (Pereira
et al., 2010; Akçakaya et al., 2015). This paper provides an overview of
the scenarios, models and metrics used in this intercomparison, and thus a
roadmap for further analyses that is envisaged to be integrated into the
first global assessment of the IPBES (Fig. 1).

All the models included in BES-SIM used the same set of scenarios with
particular combinations of SSPs and RCPs. In the selection of the scenarios,
we applied the following criteria: (1) data on projections should be readily
available, and (2) the total set should cover a broad range of land-use
change and climate change projections. The first criterion entailed the
selection of SSP-RCP combinations that are included in the ScenarioMIP
protocol as part of CMIP6 (O'Neill et al., 2016), as harmonized data were
available for these runs and they form the basis of the CMIP climate
simulations. The second criterion implied a selection of scenarios with low
and high degrees of climate change and different land-use scenarios within
the ScenarioMIP set. Our final selection was SSP1 with RCP2.6 (moderate
land-use pressure and low level of climate change) (van Vuuren et al., 2017),
SSP3 with RCP6.0 (high land-use pressure and moderately high level of climate
change) (Fujimori et al., 2017), and SSP5 with RCP8.5 (medium land-use
pressure and very high level of climate change) (Kriegler et al., 2017), thus
allowing us to assess a broad range of plausible futures (Table 1). Further,
by combining projections of low and high anthropogenic pressure on land use
with low and high levels of climate change, we can test these drivers'
individual and synergistic impacts on biodiversity and ecosystem services.

The first scenario (SSP1xRCP2.6) is characterized by a relatively
“environmentally friendly world” with low population growth, high
urbanization, relatively low demand for animal products, and high
agricultural productivity. These factors together lead to a decrease in the
land use of around 700 Mha globally over time (mostly pastures). This
scenario is also characterized by low air pollution, as policies are
introduced to limit the increase in greenhouse gases in the atmosphere,
leading to an additional forcing of 2.6 W m−2 before 2100. The second
scenario (SSP3xRCP6.0) is characterized by “regional rivalry”, with high
population growth, slow economic development, material-intensive consumption,
and low food demand per capita. Agricultural land intensification is low,
especially due to the very limited transfer of new agricultural technologies
to developing countries. This scenario has minimal land-use change
regulation, with a large land conversion for human-dominated uses, and a
relatively high level of climate change with a radiative forcing of
6.0 W m−2 by 2100. The third scenario (SSP5xRCP8.5) is a world
characterized by “strong economic growth” fuelled by fossil fuels, with low
population growth, high urbanization, and high food demand per capita but
also high agricultural productivity. As a result, there is a modest increase
in land use. Air pollution policies are stringent, motivated by local health
concerns. This scenario leads to a very high level of climate change with a
radiative forcing of 8.5 W m−2 by 2100. Full descriptions of each SSP
scenario are provided in Popp et al. (2017) and Riahi et al. (2017). The SSP
scenarios excluded elements that have interaction effects with climate change
except for SSP1, which focuses on environmental sustainability. Thus, SSPs
describe futures where biodiversity is not affected by climate change to
allow for the important estimation of the climate change impact on
biodiversity (O'Neill et al., 2014).

Table 2Improvements made in the Land Use Harmonization v2 (LUH2) from LUH
v1 (sources: Hurtt et al., 2011, 2018).

A consistent set of land-use and climate data was implemented across the
models to the extent possible. All models in BES-SIM used the newly released
Land Use Harmonization version 2 dataset (LUH2, Hurtt et al., 2018).
For the models that require climate data, we selected the climate projections
of the past, present, and future from CMIP5/ISIMIP2a
(McSweeney and Jones, 2016) and its downscaled version from
the WorldClim (Fick and Hijmans, 2017), as well as MAGICC 6.0 (Meinshausen et
al., 2011a, b) from the IMAGE model for GLOBIO models (Table 2). A complete
list of input datasets and variables used by the models is documented in
Table S1 of the Supplement.

3.1 Land-cover and land-use change data

The land-use scenarios provide an assessment of land-use dynamics in response
to a range of socio-economic drivers and their consequences for the land
system. The IAMs used for modeling land-use scenarios – IMAGE for
SSP1/RCP2.6, AIM for SSP3/RCP7.0, and REMIND/MAgPIE for SSP5/RCP8.5 –
include different economic and land-use modules for the translation of
narratives into consistent quantitative projections across scenarios (Popp et
al., 2017). It is important to note that the used land-use scenarios,
although driven mostly by the SSP storylines, were projected to be consistent
with the paired RCPs and include biofuel deployment to mitigate climate
change. The SSP3 is associated with RCP7.0 (SSP3xRCP7.0); however, climate
projections (i.e., time series of precipitation and temperature) are
currently not available for RCP7.0. Therefore, we chose the closest RCP
available, which was RCP6.0, for the standalone use of climate projections,
and chose SSP3xRCP6.0 for the land-use projections from the LUH2. In this
paper, we refer to this scenario as SSP3xRCP6.0.

For biodiversity and ecosystem services models that rely on discrete,
high-resolution land-use data (i.e., the GLOBIO model for terrestrial
biodiversity and the InVEST model), the fractional LUH2 data were downscaled
to discrete land-use grids (10 arcsec resolution; ∼300 m) with the
land-use allocation routine of the GLOBIO4 model. To that end, urban,
cropland, pasture, rangeland, and forestry areas from LUH2 were first
aggregated across the LUH2 grid cells to the regional level of the IMAGE
model, with forestry consisting of the wood harvest from forested cells and
non-forested cells with primary vegetation. Next, the totals per region were
allocated to 300 m cells with the GLOBIO4 land allocation routine, with
specific suitability layers for urban, cropland, pasture, rangeland, and
forestry areas. After allocation, cropland was reclassified into three
intensity classes (low, medium, high) based on the amount of fertilizer used
per grid cell. More details on the downscaling procedure are provided in
Supplementary Methods in the Supplement.

3.2 Climate data

GCMs are based on fundamental physical processes (e.g., conservation of
energy, mass, and momentum and their interaction with the climate system) and
simulate climate patterns of temperature, precipitation, and extreme events
on a large scale (Frischknecht et al., 2016). Some GCMs now incorporate
elements of Earth's climate system (e.g., atmospheric chemistry, soil and
vegetation, land and sea ice, carbon cycle) in Earth system models (GCMs with
an interactive carbon cycle), and have dynamically downscaled models with
higher-resolution data in regional climate models (RCMs).

A large number of climate datasets are available today from multiple GCMs,
but not all GCMs provide projections for all RCPs. In BES-SIM, some models
require continuous time-series data. In order to harmonize the climate data
to be used across biodiversity and ecosystem services models, we chose the
bias-corrected climate projections from CMIP5, which were also adopted by
ISIMIP2a (Hempel et al., 2013) or their downscaled versions available from
WorldClim (Fick and Hijmans, 2017). Most analyses were carried out using a
single GCM, the IPSL-CM5A-LR (Dufresne et al., 2013), since it provides
mid-range projections across the five GCMs (HadGEM2-ESGFDL-ESM2M,
IPSL-CM5A-LR, MIROC-ESM-CHEM, and NorESM1-M) in ISIMIP2a (Warszawski et al.,
2014).

The ISIMIP2a output from the IPSL-CM5A-LR provides 12 climate variables on
daily time steps from the pre-industrial period 1951 to 2099 at 0.5∘
resolution (McSweeney and Jones, 2016), of which only a subset was used in
this exercise (Table S1). The WorldClim downscaled dataset has 19 bioclimatic
variables derived from monthly temperature and rainfall from 1960 to 1990
with multi-year averages for specific points in time (e.g., 2050, 2070) up to
2070. Six models in BES-SIM used the ISIMIP2a dataset and three models used
the WorldClim dataset. An exception was made for the GLOBIO models, which
used MAGICC 6.0 climate data (Meinshausen et al., 2011a, b) in the IMAGE
model framework (Stehfest et al., 2014), to which GLOBIO is tightly connected
(Table 3). The variables used from the climate dataset in each model are
listed in Table S1.

3.3 Other input data

In addition to the land-use and climate data, most models use additional
input data to run their future and past simulations to estimate changes in
biodiversity and ecosystem services. For instance, species occurrence data
are an integral part of modeling in 6 of 10
biodiversity models, while 2 models rely on estimates of habitat affinity
coefficients (e.g., reductions in species richness in a modified habitat
relative to the pristine habitat) from the PREDICTS model (Newbold et al.,
2016; Purvis et al., 2018). In three dynamic global vegetation models
(DGVMs), atmospheric CO2 concentrations, irrigated fraction, and
wood harvest estimates are commonly used, while two ecosystem services models
rely on topography and soil-type data for soil erosion measures. A full list
of model-specific input data is given in Table S1.

Biodiversity and ecosystem services models at the global scale have increased
in number and improved considerably over the last decade, especially with the
availability of biodiversity data and advancement in statistical modeling
tools and methods (IPBES, 2016). In order for a model to be included in
BES-SIM, it had either to be published in a peer-reviewed journal or adopt
published methodologies, with modifications made to modeling sufficiently
documented and accessible for review (Table S2). Sixteen models were included
in BES-SIM (Appendix A, details on modeling methods in Table S2). These
models were mainly grouped into four classes: species-based, community-based,
and ecosystem-based models of biodiversity, and models of ecosystem functions
and services. The methodological approaches, the taxonomic or functional
groups, the spatial resolution and the output metrics differ across models
(Appendix A). All 16 models are spatially explicit, with 15 of them using
land-use data as an input and 13 of them requiring climate data. We also used
one model, BIOMOD2 (Thuiller, 2004; Thuiller et al., 2009), to assess the
uncertainty of climate range projections without the use of land-use data.

4.1 Species-based models of biodiversity

Species-based models aim to predict historical, current, and future potential
distribution and abundance of individual species. These can be developed
using correlative methods based on species observation and environmental data
(Aguirre-Gutiérrez et al., 2013; Guisan and Thuiller, 2005; Guisan and
Zimmermann, 2000) as well as expert-based solutions where data limitations
exist (Rondinini et al., 2011). Depending on the methodologies employed and
the ecological aspects modeled, they can be known as species distribution
models, ecological niche models, bioclimatic
envelope models, and habitat suitability models
(Elith and Leathwick, 2009). Such species-based models have been used to
forecast environmental impacts on species distribution and status.

In BES-SIM, four species-based models were included: AIM-biodiversity (Ohashi
et al., 2018), InSiGHTS (Rondinini et al., 2011; Visconti et al., 2016), MOL
(Jetz et al., 2007; Merow et al., 2013), and BIOMOD2 (Appendix A, Table S2).
The first three models project individual species distributions across a
large number of species by combining projections of climate impacts on
species ranges with projections of land-use impacts on species ranges.
AIM-biodiversity uses Global Biodiversity Information Facility (GBIF) species
occurrence data on 9025 species across five taxonomic groups (amphibians,
birds, mammals, plants, reptiles) to train statistical models for current
land use and climate to project future species distributions. InSiGHTS uses
species' presence records from regular sampling within species' ranges and
pseudo-absence records from regular sampling outside of species' ranges on
2827 species of mammals. MOL uses species land-cover preference information
and species presence and absence predictions on 20 833 species of
amphibians, birds, and mammals. InSiGHTS and MOL rely on IUCN's range maps as
a baseline, which are developed based on expert knowledge of the species
habitat preferences and areas of non-occurrence (Fourcade, 2016). Both
models use a hierarchical approach with two steps: first, a statistical model
trained on current species ranges is used to assess future climate
suitability within species ranges; second, a model detailing associations
between species and habitat types based on expert opinion is used to assess
the impacts of land use in the climate-suitable portion of the species range.
BIOMOD2 is an R modeling package that runs up to nine different algorithms
(e.g., random forests, logistic regression) of species distribution models
using the same data and the same framework. BIOMOD2 included three taxonomic
groups (amphibians, birds, mammals) (see Sect. 7 “Uncertainties”).

4.2 Community-based models of biodiversity

Community-based models predict the assemblage of species using environmental
data and assess changes in community composition through species presence and
abundance (D'Amen et al., 2017). Output variables of community-based models
include assemblage-level metrics, such as the proportion of species
persisting in a landscape, mean species abundances (number of individuals per
species), and compositional similarity (pairwise comparison at the species
level) relative to a baseline (typically corresponding to a pristine
landscape).

Three models in BES-SIM – cSAR-iDiv (Martins and Pereira, 2017),
cSAR-IIASA-ETH (Chaudhary et al., 2015), and BILBI (Hoskins et al., 2018;
Ferrier et al., 2004, 2007) – rely on versions of the species–area
relationship (SAR) to estimate the proportion of species persisting in
human-modified habitats relative to native habitat (i.e., the number of
species in the modified landscape divided by the number of species in the
native habitat). In its classical form, the SAR describes the relationship
between the area of native habitat and the number of species found within
that area. The countryside SAR (cSAR) builds on the classic SAR but accounts
for the differential use of both human-modified and native habitats by
different functional species groups. Both the cSAR-iDiv and cSAR-IIASA-ETH
models use habitat affinities (proportion of area of a habitat type that can
be effectively used by a species group) to weight the areas of the different
habitats in a landscape. The habitat affinities are calibrated from field
studies by calculating the change in species richness in a modified habitat
relative to the native habitat. The habitat affinities of the cSAR-iDiv model
are estimated from the PREDICTS dataset (Hudson et al., 2017, 2016) while the
habitat affinities of cSAR-IIASA-ETH come from a previously published
database of studies (Chaudhary et al., 2015). The cSAR-iDiv model considers
9853 species for one taxonomic group (birds) in two functional groups (forest
species and non-forest species) while cSAR-IIASA-ETH considers a total of
1 911 583 species for five taxonomic groups (amphibians, birds, mammals,
plants, reptiles) by ecoregions (these are, however, not 1 911 583 unique
species as a species present in two ecoregions will be counted twice). BILBI
couples application of the species–area relationship with correlative
statistical modeling of continuous spatial turnover patterns in the species
composition of communities as a function of environmental variation. Through
space-for-time projection of compositional turnover (i.e., change in
species), this coupled model enables the effects of both climate change and
habitat modification to be considered in estimating the proportion of species
persisting for 254 145 vascular plant species globally.

Three community-based models – PREDICTS, GLOBIO Aquatic (Alkemade et al.,
2009; Janse et al., 2015), and GLOBIO Terrestrial (Alkemade et al., 2009;
Schipper et al., 2016) – estimate a range of assemblage-level metrics based
on empirical dose–response relationships between pressure variables (e.g.,
land-use change and climate change) and biodiversity variables (e.g., species
richness or mean species abundance) (Appendix A). PREDICTS uses a
hierarchical mixed-effects model to assess how a range of site-level
biodiversity metrics respond to land use and related pressures, using a
global database of 767 studies, including over 32 000 sites and
51 000 species from a wide range of taxonomic groups (Hudson et al., 2017,
2016). GLOBIO is an integrative modeling framework for aquatic and
terrestrial biodiversity that builds upon correlative relationships between
biodiversity intactness and pressure variables, established with
meta-analyses of biodiversity data retrieved from the literature on a wide
range of taxonomic groups.

4.3 Ecosystem-based model of biodiversity

The Madingley model (Harfoot et al., 2014b) is a mechanistic individual-based
model of ecosystem structure and function. It encodes a set of fundamental
ecological principles to model how individual heterotrophic organisms with a
body size greater than 10 µg that feed on other living organisms
interact with each other and with their environment. The model is general in
the sense that it applies the same set of principles for any ecosystem to
which it is applied, and is applicable across scales from local to global. To
capture the ecology of all organisms, the model adopts a functional
trait-based approach with organisms characterized by a set of categorical
traits (feeding mode, metabolic pathway, reproductive strategy, and movement
ability), as well as continuous traits (juvenile, adult, and current body
mass). Properties of ecological communities emerge from the interactions
between organisms, influenced by their environment. The functional diversity
of these ecological communities can be calculated, as well as the
dissimilarity over space or time between communities (Table S2). Madingley
uses three functional groups (trophic levels, metabolic pathways, and
reproductive strategies).

Table 4Selected output indicators for intercomparison of biodiversity and
ecosystems models. For species diversity change, both proportional changes in
species richness (P) and absolute changes (N) are reported. Some models
project the α metrics at the level of the grid cell (e.g.,
species-based and SAR based community models) while others average the local
values of the metrics across the grid cell weighted by the area of the
different habitats in the cell (e.g., PREDICTS, GLOBIO).

The InVEST suite includes 18 models that map and measure the flow and value
of ecosystem goods and services across a landscape or a seascape. They are
based on biophysical processes of the structure and function of ecosystems,
and they account for both supply and demand. The GLOBIO model estimates
ecosystem services based on outputs from the IMAGE model (Stehfest et al.,
2014), the PCRaster Global Water Balance global hydrological model
(PCR-GLOBWB, van Beek et al., 2011), and the Global Nutrient Model (Beusen et
al., 2015). It is based on correlative relationships between ecosystem
functions and services, and particular environmental variables (mainly land
use), quantified based on literature data. Finally, GLOSP is a 2-D model that
estimates the level of global and local soil erosion, and protection using
the Universal Soil Loss Equation.

Given the diversity of modeling approaches, a wide range of biodiversity and
ecosystem services metrics can be produced by the model set (Table S2). For
the biodiversity model intercomparison analysis, three main categories of
common output metrics were reported over time: extinctions as absolute change
in species richness (N, number of species) or as proportional species
richness change (P, % species), abundance-based intactness (I, %
intactness), and mean proportional change in suitable habitat extent across
species (H, % suitable habitat) (Table 4). These metrics were calculated
at two scales: local or grid cell (α scale, i.e., the value of the
metric within the smallest spatial unit of BES-SIM which is the grid cell)
and regional or global scale (γ scale, i.e., the value of the metric
for a set of grid cells comprising a region). For species richness change,
some models project the α metrics at the grid cell level (e.g.,
species-based and SAR-based community models), while others average the local
point values of the metrics across the grid cell weighted by the area of the
different habitats in the cell (e.g., PREDICTS, GLOBIO). In addition, some
models only provided α values while others provided both α
and γ values (Table 4). For the models that can project γ
metrics, both regional-γ for each IPBES regions (Table 1 in Brooks et
al., 2016; UNEP-WCMC, 2015) and a global-γ were reported.

The species diversity change metrics measured as absolute number or
percentage change in species richness show species persistence and extinction
in a given time and place. Absolute changes in species richness and
proportional species richness change are interrelated and may be calculated
from reporting species richness over time, as Nt=St-St0 and P=Nt/St0, where St is the number of species at time t. Most
models reported one or both types of species richness metrics (Table 4). The
abundance-based intactness (I) measures the mean species abundance in the
current community relative to the abundances in a pristine community. This
metric is available only for two community-based models: GLOBIO (where
intactness is estimated as the arithmetic mean of the abundance ratios of the
individual species, whereby ratios >1 are set to 1) and PREDICTS (where
intactness is estimated as the ratios of the sum of species abundances). The
habitat change (H) measures cell-wise changes in available habitat for the
species. It represents the changes in the suitable habitat extent of each
species relative to a baseline, i.e., (Ei,t-Ei,t0)/Ei,t0, where
Ei,t is the suitable habitat extent of species i at time t within
the unit of analysis. It is reported by averaging across species occurring in
each unit of analysis (grid cell, region, or globe), and is provided by the
species-level models (i.e., AIM-biodiversity, InSiGHTS, MOL) (Table 4). The
baseline year, t0, used to calculate changes for the extinction and
habitat extent metrics, was the first year of the simulation (in most cases
t0=1900; see Table 5).

For ecosystem functions and services, each model's output metrics were mapped
onto the new classification of Nature's Contributions to People (NCP)
published by the IPBES scientific community (Díaz et al., 2018). Among
the 18 possible NCPs, the combination of models participating in BES-SIM was
able to provide measures for 10 NCPs, including regulating metrics on
pollination (e.g., proportion of agricultural lands whose pollination needs
are met, % agricultural area), climate (e.g., vegetation carbon, total
carbon uptake and loss, MgC), water quantity (e.g., monthly runoff,
Pg month−1), water quality (e.g., nitrogen and phosphorus leaching,
PgN s−1), soil protection (e.g., erosion protection, 0–100 index),
hazards (e.g., costal vulnerability, unitless score; flood risk, number of
people affected) and detrimental organisms (e.g., fraction of cropland
potentially protected by the natural pest relative to all available cropland,
km2), and material metrics on bioenergy (e.g., bioenergy–crop
production, PgC yr−1), food and feed (e.g., total crop production,
109 KCal) and materials (e.g., wood harvest, KgC) (Table 6). Some of
these metrics require careful interpretation in the context of NCPs (e.g., an
increase in flood risk can be caused by climate change and/or by a reduction
of the capacity of ecosystems to reduce flood risk) and additional
translation of increasing or declining measures of ecosystem functions and
services (e.g., food and feed, water quantity) into contextually relevant
information (i.e., positive or negative impacts) on human well-being and
quality of life. Given the disparity of metrics across models within each NCP
category, names of the metrics are listed in Table 6, and units, definitions,
and methods are provided in Table S3.

Table 6Selected output indicators for inter-comparison of ecosystem
functions and services models, categorized based on the classification of
Nature's Contributions to People (Díaz et al., 2018).

The simulations for BES-SIM required a minimum of two outputs from the
modeling teams: present (2015) and future (2050). Additionally, a past
projection (1900) and a further future projection (2070) were also provided
by several modeling teams. Some models projected further into the past and
also at multiple time points from the past to the future (Appendix A). Models
that simulated a continuous time series of climate change impacts provided
20-year averages around these mid-points to account for inter-annual
variability. The models ran simulations at their original spatial resolutions
(Appendix A), and upscaled results to 1∘ grid cells using arithmetic
means. In order to provide global or regional averages of the α or
grid cell metrics, the arithmetic mean values across the cells of the globe
or a certain region were calculated, as well as percentiles of those metrics.
Both 1∘ rasters and a table with values for each IPBES region and the
globe were provided by each modeling team for each output metric.

To measure the individual and synergistic impacts of land-use and climate
change on biodiversity and ecosystem services, models accounting for both
types of drivers were run three times: with land-use change only, with
climate change only, and with both drivers combined. For instance, to
measure the impact of land use alone, the projections into 2050 were
obtained while retaining climate data constant from the present (2015) to
the future (2050). Similarly, to measure the impact of climate change alone,
the climate projections into 2050 (or 2070) were obtained while retaining
the land-use data constant from the present (2015) to the future (2050).
Finally, to measure the impact of land-use and climate change combined,
models were run using projections of both land-use and climate change into
2050 (or 2070). When models required continuous climate time-series data to
hindcast to 1900, data from years in the time period 1951 to 1960 were
randomly selected to fill the data missing for years 1901 to 1950 from the
ISIMIP 2a IPSL dataset. Models that used multi-decadal climate averages from
WorldClim (i.e., InSiGHTS, BILBI) assumed no climate impacts for 1900.

Reporting uncertainty is a critical component of model intercomparison
exercises (IPBES, 2016). Within BES-SIM, uncertainties were explored by each
model reporting the mean values of its metrics, and where possible the 25th,
50th, and 75th percentiles based on the parameterization set specific to each
model, which can be found in each model's key manuscripts describing the
modeling methods. When combining the data provided by the different models,
the average and the standard deviations of the common metrics were calculated
(e.g., intermodel average and standard deviation of Pγ). In a
parallel exercise to inform BES-SIM, the BIOMOD2 model was used in assessing
the uncertainty in modeling changes in species ranges arising from using
different RCP scenarios, different GCMs, a suite of species distribution
modeling algorithms (e.g., random forest, logistic regression), and different
species dispersal hypotheses.

The existing SSP and RCP scenarios provide a consistent set of past and
future projections of two major drivers of terrestrial and freshwater
biodiversity change – land use and climate. However, we acknowledge that
these projections have certain limitations. These include limited
consideration of biodiversity-specific policies in the storylines (only the
SSP1 baseline emphasizes additional biodiversity policies) (O'Neill et al.,
2016; Rosa et al., 2017), coarse spatial resolution, and land-use classes
that are not sufficiently detailed to fully capture the response of
biodiversity to land-use change (Harfoot et al., 2014a; Titeux et al., 2016,
2017). The heterogeneity of models and their methodological approaches, as
well as additional harmonization of metrics of ecosystem functions and
services (Tables 6, S3), are areas for further work. In the future, it will
also be important to capture the uncertainties associated with input data,
with a focus on uncertainty in land-use and climate projections resulting
from differences among IAMs and GCMs on each scenario (Popp et al., 2017).
The gaps identified through BES-SIM and future directions for research and
modeling will be published separately, as well as analyses of the results on
the model intercomparison and on individual models.
As a
long-term perspective, BES-SIM is expected to provide critical foundation and
insights for the ongoing development of nature-centred, multiscale Nature
Futures scenarios (Rosa et al., 2017). Catalyzed by the IPBES Expert Group on
Scenarios and Models, this new scenario and modeling framework will shift
traditional ways of forecasting impacts of society on nature to more
integrative, biodiversity-centred visions and pathways of socio-economic and
ecological systems. A future round of BES-SIM could use these
biodiversity-centred storylines to project dynamics of biodiversity and
ecosystem services and associated consequences for socio-economic development
and human well-being. This will help policymakers and practitioners to
collectively identify pathways for sustainable futures based on alternative
biodiversity management approaches and assist researchers in incorporating
the role of biodiversity into socio-economic scenarios.

All the authors co-designed the study and provided scientific input and technical details on models, scenarios and data necessary to carry out the
intermodel comparison and synthesis. HMP, RA, PL, and IMDR led the development of the protocol, and HK
led the writing of the manuscript with model-specific text contributions and review comments from all co-authors.

HyeJin Kim, Inês Santos Martins, Florian Wolf, Carlos Guerra, and Henrique
M. Pereira are supported by the German Centre for Integrative Biodiversity
Research (iDiv) Halle-Jena-Leipzig, funded by the German Research Foundation
(FZT 118). Isabel Maria Duarte Rosa acknowledges funding from the European
Union's Horizon 2020 research and innovation program under Marie
Sklodowska-Curie grant agreement no. 703862. Paul Leadley is supported by the
LabEx BASC supported by the French “Investment d'Avenir” program (grant
ANR-11-LABX-0034). George C. Hurtt and Louise Parsons Chini gratefully
acknowledge the support of the DOE-SciDAC program (DE SC0012972). Almut
Arneth, Andreas Krause, Benjamin Quesada, and Peter Anthoni acknowledge
support from the Helmholtz Association and its ATMO Programme, and EU FP7
project LUC4C. Andy Purvis, Adriana De Palm, and Samantha L. L. Hill are
supported by the Natural Environment Research Council U.K. (grant number
NE/M014533/1) and by a DIF grant from the Natural History Museum. Rebecca
Chaplin-Kramer and Richard Sharp are supported by private gifts to the
Natural Capital Project. David Leclère, Fulvio Di Fulvio, Petr Havlík,
and Michael Obersteiner are supported by the project IS-WEL-Integrated
Solutions for Water, Energy and Land funding from the Global Environmental
Facility, Washington, USA, coordinated by the United Nations Industrial
Development Organization (UNIDO), UNIDO project no. 140312. Fulvio Di Fulvio
and Michael Obersteiner are supported by the ERC SYNERGY grant project
IMBALANCE-P-Managing Phosphorous limitation in a nitrogen-saturated
Anthropocene, funding from the European Commission, European Research Council
Executive Agency, grant agreement no. 610028. David Leclère and Petr
Havlík are supported by project SIGMA – Stimulating Innovation for Global
Monitoring of Agriculture – and its Impact on the Environment in support of
GEOGLAM, funding from the European Union's FP7 research and innovation
program under the environment area, grant agreement no. 603719. Tomoko
Hasegawa, Haruka Ohashi, Akiko Hirata, Shinichiro Fujimori, Tetsuya Matsui,
and Kiyoshi Takahashi are supported by the Global Environmental Research
(S-14) of the Ministry of the Environment of Japan. Tomoko Hasegawa,
Shinichiro Fujimori, and Kiyoshi Takahashi are supported by Environment
Research and Technology Development Fund 2-1702 of the Environmental
Restoration and Conservation Agency of Japan. Mike Harfoot is supported by a
KR Rasmussen Foundation grant “Modelling the Biodiversity Planetary Boundary
and Embedding Results into Policy” (FP-1503-01714). Vanessa Haverd
acknowledges support from the Earth Systems and Climate Change Hub, funded by
the Australian Government's National Environmental Science program. Cory
Merow acknowledges funding from NSF grant DEB1565046. Finally, we also thank
the following organizations for funding the workshops: the PBL Netherland
Environment Assessment Agency, UNESCO (March 2016), the iDiv German Centre
for Integrative Biodiversity Research (October 2016, October 2017), and the
Zoological Society of London (January 2018).

Secretariat of the Convention on Biological Diversity and United Nations
Environment Programme (Eds.): Global biodiversity outlook 4: a mid-term
assessment of progress towards the implementation of the strategic plan for
biodiversity 2011–2020, Secretariat for the Convention on Biological
Diversity, Montreal, Quebec, Canada, 2014.

This paper lays out the protocol for the Biodiversity and Ecosystem Services Scenario-based Intercomparison of Models (BES-SIM) that projects the global impacts of land use and climate change on biodiversity and ecosystem services over the coming decades, compared to the 20th century. BES-SIM uses harmonized scenarios and input data and a set of common output metrics at multiple scales, and identifies model uncertainties and research gaps.

This paper lays out the protocol for the Biodiversity and Ecosystem Services Scenario-based...